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Enforcing biconnectivity in multi-robot systems

Authors: Zareh Eshghdoust, Mehran; Sabattini, Lorenzo; Secchi, Cristian;

Enforcing biconnectivity in multi-robot systems

Abstract

Connectivity maintenance is an essential task in multi-robot systems and it has received a considerable attention during the last years. A connected system can be broken into two or more subsets simply if a single robot fails. A more robust communication can be achieved if the network connectivity is guaranteed in the case of one-robot failures. The resulting network is called biconnected. In \cite{Zareh2016biconnectivitycheck}, we presented a criterion for biconnectivity check, which basically determines a lower bound on the third-smallest eigenvalue of the Laplacian matrix. In this paper, we introduce a decentralized gradient-based protocol to increase the value of the third-smallest eigenvalue of the Laplacian matrix, when the biconnectivity check fails. We also introduce a decentralized algorithm to estimate the eigenvectors of the Laplacian matrix, which are used for defining the gradient. Simulations show the effectiveness of the theoretical findings.

arXiv admin note: text overlap with arXiv:1608.02276

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Italy
Keywords

FOS: Computer and information sciences, Systems and Control (eess.SY), Dynamical Systems (math.DS), Electrical Engineering and Systems Science - Systems and Control, Computer Science - Robotics, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Computer Science - Multiagent Systems, Mathematics - Dynamical Systems, Robotics (cs.RO), Artificial Intelligence; Decision Sciences (miscellaneous); Control and Optimization, Multiagent Systems (cs.MA)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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